Identity recognition performed by biometric systems is not a binary task. In particular, in determining whether a person is or is not the person he or she claims to be, biometric systems cannot provide an absolute answer. Rather, biometric systems need to utilize a sliding scale where a similarity of two persons' biometric data samples (in the following also referred to as samples) is determined with a certain level of confidence. Such a degree of similarity can be provided by a numerical value that may be called a matching score. The matching score may vary between two boundaries, wherein a first boundary may represent a case where two biometric data samples are not similar and a second boundary may represent a case where the two biometric data samples are identical. In order to make a decision as to whether the two biometric data samples are a match or not, a matching threshold may be determined. The matching threshold may define whether a matching score indicates a match, i.e. the biometric data samples are similar, or a non-match, i.e. the biometric data are not similar.
For example, matching score boundaries may be defined on a scale of [0-100] where 100 may represent a perfect match and 0 may represent a non-match. An exemplary matching threshold may be set at 75. Accordingly, any matching score above this threshold may be considered as a match and any matching score below this threshold may be considered as a non-match. The closer the matching score is to the upper boundary, the more certain a match is.
In addition to the uncertainty in biometric identity recognition as outlined above, changes in the quality of captured biometric data samples, changes in environmental conditions, and/or fraud attempts, can also affect a matching score. Accordingly, a situation where the matching score of two biometric data samples of the same person is below the matching threshold may be defined as a “False Rejection”, or “False Non-Match”, and the situation where the matching score of two biometric data samples of two distinct persons is above the matching threshold is defined as “False Acceptance” or “False Match”.
In practice, it is not possible to predefine a perfect matching threshold for a biometric system. Therefore, for each biometric system, a threshold value is typically defined once by performing a benchmark test/benchmarking on a representative biometric data sample before deployment of the biometric system.
However, during operation of the biometric system, deviations from expected conditions may appear, e.g. in data quality, in environmental conditions, and/or in subject demographics. Accordingly, in conventional biometric systems, a problem arises that in such situations 1) the security of the biometric system can decrease due to false acceptance of non-matching biometric data samples, or 2) the biometric system may become unusable due to false rejection of matching biometric data samples. For example, when using a facial recognition system, e.g. in an Automatic Border Control (ABC) eGate, an automated identification or verification of persons passing a border may be performed. Identification and/or verification may be performed using digital images or video frames of a video source may be performed. Such identification and/or verification may be performed by comparing selected facial biometric identifiers from the image (i.e. a biometric data sample) with facial biometric identifiers (i.e. biometric data samples) stored in a corresponding database. However, environmental conditions, e.g. a lighting intensity or angles, in the area of the ABC eGate may vary throughout a day. Such environmental conditions may become a factor in a False Acceptance Rate (FAR, also called False Match Rate, FMR) or False Rejection Rate (FRR, also called False Non-Match Rate, FNMR) of the facial recognition system. As another example, passenger demographics at the border using the ABC eGate may change e.g. due to planes arriving from different continents. This might also become a factor in the FAR or FRR of the facial recognition system.
In order to address these problems, conventional biometric systems use a static matching threshold value that may be manually fine-tuned by a system administrator during benchmarking or operation of the biometric system. In a further example, the biometric system may be monitored by a monitoring system. If the monitoring system observes unacceptable values for the FAR and/or the FRR, an alarm message for the system administrator of the biometric system may be triggered. Upon receiving the alarm message, the system administrator may manually fine-tune the matching threshold.
Accordingly, there is a need for an improved biometric system that can adapt to changes surrounding the biometric system, e.g. changes in data quality, in environmental conditions and/or in subject demographics in order to prevent fraud attempts or single incidents more efficiently.